FFT-based Identification of Data Loss Models

Recently measurement data loss has been of greater interest, due to the spread of sensor networks and the idea of Internet of things. A procedure is proposed that is able to identify the most frequently employed data loss models. It is assumed that the communication protocol provides information about data loss, i.e. the so-called data availability indicator function is known. The power spectral density (PSD) of the indicator function is representative for the model, and can be used for identification. Spectral estimation is carried out by Fast Fourier Transform (FFT) based techniques. The paper introduces the identification procedure for random independent, random block-based and a Markov model-based data loss patterns. The efficiency of the proposed method is demonstrated by simulation and measurement results.